The authors present methods to reduce computer energy consumption by means of a better usage of a specific set of resources and maximizing the efficiency of the running applications. The processor frequency is adjusted to the needs of the running job, leading to a power drop by a factor of 2 and doubling battery life time of laptops. It is shown how computer resources can be optimally adapted to application needs, reducing job run time. Examples on how to optimize algorithms on single node and parallel RISC architectures are discussed. The job-related data are stored and reused to help computer managers to replace machines.

Making the most efficient use of computer systems has rapidly become a leading topic of interest for the computer industry and its customers alike. However, the focus of these discussions is often on single, isolated, and specific architectural and technological improvements for power reduction and conservation, while ignoring the fact that power efficiency as a ratio of performance to power consumption is equally influenced by performance improvements and architectural power reduction. Furthermore, efficiency can be influenced on all levels of today’s system hierarchies from single cores all the way to distributed Grid environments. To improve execution and power efficiency requires progress in such diverse fields as program optimization, optimization of program scheduling, and power reduction of idling system components for all levels of the system hierarchy.